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Machine learning (ML) techniques are increasingly being used in clinical medical imaging to automate distinct processing tasks. In post-mortem forensic radiology, the use of these algorithms presents significant challenges due to variability in organ position, structural changes from decomposition, inconsistent body placement in the scanner, and the presence of foreign bodies. Existing ML approaches in clinical imaging can likely be transferred to the forensic setting with careful consideration to account for the increased variability and temporal factors that affect the data used to train these algorithms. Additional steps are required to deal with these issues, by incorporating the possible variability into the training data through data augmentation, or by using atlases as a pre-processing step to account for death-related factors. A key application of ML would be then to highlight anatomical and gross pathological features of interest, or present information to help optimally determine the cause of death. In this review, we highlight results and limitations of applications in clinical medical imaging that use ML to determine key implications for their application in the forensic setting., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Introduction: An immersive virtual reality (VR) simulation clinic with dynamic patient interaction and communication was developed to facilitate the training of medical radiation science students. The software "CETSOL VR Clinic" was integrated into the Medical Imaging programme at Monash University in 2016 in order to benchmark student experiences against existing simulation techniques (Shaderware™)., Methods: An iterative approach to development, based on two cycles of user feedback, was used to develop and refine the simulated clinical environment. This environment uses realistic 3D models, embedded clinical scenarios, dynamic communication, 3D hand gesture interaction, gaze and positional stereoscopic tracking and online user capabilities using the Unity™ game and physics engines. Students' perceptions of educational enhancement of their positioning skills following the use of the simulation tools were analysed via a 5-point Likert scale questionnaire., Results: Student perception scores indicated a significant difference between simulation modalities in favour of the immersive CETSOL VR Clinic, χ 2 (4, N = 92) = 9.5, P-value <0.001., Conclusion: Student perception scores on improvement of their clinical and technical skills were higher for the hand-positioning tasks performed with the CETSOL VR Clinic™ than with the comparative benchmark simulation that did not provide dynamic patient interaction and communication., (© 2018 The Authors. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.)